store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 1000, 'test_batch_size': 1000, 'validation_set_size': 1000, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 10, 'num_iterations': 100, 'no_cuda': False, 'name': 'bald_20_247293', 'seed': 247293, 'log_interval': 10, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[51327, 2826, 27329, 46916, 33373, 42821, 49103, 47605, 5356, 1932, 3031, 26018, 58329, 8454, 1946, 43023, 26244, 39658, 48633, 17591]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6504, 'nll': 2.2559166669845583}, 'chosen_samples': [13018, 35776, 16123, 34944, 12483, 6151, 34872, 24831, 8238, 24724], 'chosen_samples_score': ['1.1444783', '1.1573176', '1.1693007', '1.1568787', '1.1447698', '1.1767157', '1.1958308', '1.209799', '1.1890705', '1.1951938']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6618, 'nll': 2.1119736075401305}, 'chosen_samples': [4934, 620, 49085, 33816, 27209, 39522, 37270, 3947, 7990, 40970], 'chosen_samples_score': ['1.0308537', '1.043097', '1.0454907', '1.0472536', '1.0680559', '1.0700526', '1.0838193', '1.1540713', '1.1798611', '1.0930073']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7156, 'nll': 1.6401762008666991}, 'chosen_samples': [22481, 4481, 48668, 3855, 15889, 17849, 8338, 51077, 41544, 35996], 'chosen_samples_score': ['1.0195494', '1.0220178', '1.022969', '1.0238221', '1.0332177', '1.050499', '1.05687', '1.1396706', '1.0392518', '1.0519025']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7856, 'nll': 1.2210216879844666}, 'chosen_samples': [37453, 59333, 43208, 53976, 12655, 12449, 12957, 48852, 31014, 189], 'chosen_samples_score': ['1.0103323', '1.025465', '1.0217364', '1.0376436', '1.0250316', '1.0318041', '1.048996', '1.0785514', '1.1926625', '1.1783847']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7653, 'nll': 1.3044640660285949}, 'chosen_samples': [21304, 18486, 25644, 56641, 47068, 31312, 18298, 51314, 31456, 57325], 'chosen_samples_score': ['0.8974851', '0.9104085', '0.912706', '0.97716576', '0.92051727', '0.91879624', '0.9141731', '0.9802996', '0.9385127', '1.0259187']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7902, 'nll': 1.1424327850341798}, 'chosen_samples': [39487, 31915, 53262, 15016, 19505, 37676, 40766, 55612, 47723, 51764], 'chosen_samples_score': ['0.85523915', '0.8630328', '0.8672196', '0.8735976', '0.8800468', '0.8827237', '0.89614636', '0.8863856', '0.8898623', '0.97682035']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8165, 'nll': 1.0072770893573761}, 'chosen_samples': [33812, 32427, 9180, 46530, 22250, 18240, 39128, 27503, 1563, 40573], 'chosen_samples_score': ['0.77055436', '0.7725363', '0.7923757', '0.77356464', '0.8093815', '0.8158674', '0.8191979', '0.94173163', '0.8365596', '0.8700102']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.8227, 'nll': 0.9667537152767182}, 'chosen_samples': [19187, 22783, 25577, 4948, 28412, 57240, 41478, 47914, 4495, 43176], 'chosen_samples_score': ['0.7586399', '0.77242684', '0.77966195', '0.79378706', '0.8217336', '0.8285462', '0.7968689', '0.81238395', '0.79645663', '0.80891174']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8495, 'nll': 0.9536541223526}, 'chosen_samples': [51015, 33162, 42919, 7168, 53191, 16456, 57404, 16286, 15406, 5315], 'chosen_samples_score': ['0.9097893', '0.9108394', '0.9227821', '0.915157', '0.9152864', '0.9389454', '0.9770936', '1.0234144', '0.951882', '0.9481738']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.869, 'nll': 0.7175660610198975}, 'chosen_samples': [44698, 12768, 59176, 59314, 51197, 27585, 37214, 17367, 28609, 14417], 'chosen_samples_score': ['0.8645494', '0.86530495', '0.8750134', '0.8825305', '0.8886829', '0.8920939', '0.8968795', '0.9294595', '0.93313587', '1.0021532']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.901, 'nll': 0.5936939656734467}, 'chosen_samples': [29879, 47628, 55513, 48360, 49529, 47741, 35643, 8339, 47471, 39818], 'chosen_samples_score': ['0.89702433', '0.90904933', '0.9328917', '0.9335753', '0.9640415', '0.945903', '0.9618575', '0.91496694', '0.95437324', '0.9902049']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8918, 'nll': 0.6359041064977646}, 'chosen_samples': [14894, 26079, 1674, 32776, 801, 13121, 34406, 23411, 10038, 8847], 'chosen_samples_score': ['0.90776974', '0.9137833', '0.9200192', '0.9209514', '0.9910628', '0.9852519', '0.9527195', '0.9330649', '0.96691084', '0.9240351']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.902, 'nll': 0.5868744552135468}, 'chosen_samples': [9725, 24687, 47036, 59726, 55274, 32381, 11787, 262, 59747, 29725], 'chosen_samples_score': ['0.8735414', '0.8823776', '0.8896305', '0.9038351', '0.8919975', '0.91053706', '0.92557013', '0.9329539', '0.9962107', '0.9691239']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9063, 'nll': 0.580069574713707}, 'chosen_samples': [14440, 3494, 56714, 37491, 40590, 32150, 5216, 3768, 32712, 20463], 'chosen_samples_score': ['0.9437444', '0.9493583', '0.94945896', '0.95511925', '0.99855924', '1.0176328', '0.9851057', '0.96289253', '0.9778926', '1.0427424']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9077, 'nll': 0.5481114596128464}, 'chosen_samples': [50010, 42078, 8954, 14351, 2845, 57342, 9084, 38698, 34678, 3691], 'chosen_samples_score': ['0.89619493', '0.8974131', '0.9059357', '0.9099139', '0.936441', '0.94753534', '1.0327098', '0.95361745', '1.0226388', '0.93998563']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9436, 'nll': 0.41810364127159116}, 'chosen_samples': [20363, 9472, 35632, 2765, 19868, 42428, 8031, 34758, 14697, 53170], 'chosen_samples_score': ['1.0278889', '1.0295557', '1.0298798', '1.0316465', '1.0778723', '1.1134281', '1.0504513', '1.0515723', '1.047673', '1.1011574']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9272, 'nll': 0.4834138214588165}, 'chosen_samples': [41501, 57773, 17542, 12938, 19089, 41060, 30884, 51800, 52086, 33469], 'chosen_samples_score': ['0.9367153', '0.954194', '0.94886625', '0.99242043', '0.9489805', '1.0090957', '1.0535101', '0.96087027', '0.9539889', '0.96601504']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9383, 'nll': 0.45698689818382265}, 'chosen_samples': [59783, 23215, 42703, 35128, 53156, 42671, 54646, 14790, 43575, 26483], 'chosen_samples_score': ['0.9332525', '0.9363301', '0.94499475', '0.9510431', '0.96665126', '0.98096657', '0.98352516', '1.0183992', '1.0462599', '0.9682499']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9406, 'nll': 0.4350645363330841}, 'chosen_samples': [7328, 11572, 51988, 13524, 12984, 5175, 30322, 35232, 26745, 42384], 'chosen_samples_score': ['0.9913519', '1.0220287', '1.0228573', '0.9978285', '0.9975814', '1.0347147', '1.0369706', '1.1222678', '1.0853289', '1.0798488']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.937, 'nll': 0.42969619631767275}, 'chosen_samples': [34500, 19188, 32908, 29759, 6309, 55488, 5013, 19586, 53062, 57728], 'chosen_samples_score': ['0.90627223', '0.9099149', '0.9102001', '0.9166911', '0.9267852', '0.9358548', '0.9857377', '0.9316731', '0.9264806', '0.9228414']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9405, 'nll': 0.4338295668363571}, 'chosen_samples': [27793, 17486, 40184, 5161, 9147, 14896, 29179, 8458, 34946, 25318], 'chosen_samples_score': ['0.9060927', '0.90766484', '0.9236395', '0.9678125', '0.9644269', '0.9237385', '0.9338087', '1.0097884', '0.95103824', '0.9335958']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9544, 'nll': 0.38667966723442077}, 'chosen_samples': [34328, 5841, 57742, 52914, 14949, 27429, 43943, 37469, 49890, 57543], 'chosen_samples_score': ['1.0492167', '1.0561261', '1.3333553', '1.0935574', '1.1007736', '1.0866416', '1.0606452', '1.108922', '1.1575527', '1.0614842']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9514, 'nll': 0.390186482667923}, 'chosen_samples': [58874, 2803, 40702, 33224, 18247, 47655, 32047, 22470, 29591, 207], 'chosen_samples_score': ['0.92030376', '0.92794234', '0.9886895', '1.1710087', '0.92995423', '0.9716159', '0.9216276', '0.94752747', '0.9285253', '0.98898166']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.956, 'nll': 0.35061962306499483}, 'chosen_samples': [32421, 33505, 49905, 51544, 16836, 31530, 3070, 31954, 43174, 20169], 'chosen_samples_score': ['0.97619295', '0.9824921', '1.0438423', '1.1713209', '1.0399749', '0.98588985', '1.006465', '1.1945689', '1.0317237', '1.1797233']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9555, 'nll': 0.3645711049437523}, 'chosen_samples': [57523, 5790, 38050, 14619, 38567, 44143, 8447, 57882, 59731, 18487], 'chosen_samples_score': ['0.93430704', '0.94954944', '1.0027049', '0.95192575', '0.95219946', '1.0254881', '0.95681995', '0.9435289', '1.0559766', '0.9886033']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.952, 'nll': 0.3893869951367378}, 'chosen_samples': [22283, 24860, 2202, 42472, 19837, 14722, 26266, 41713, 47503, 6466], 'chosen_samples_score': ['1.034208', '1.052011', '1.055641', '1.06674', '1.1114682', '1.2187803', '1.1500537', '1.0763943', '1.0873239', '1.0724412']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9535, 'nll': 0.3737568140029907}, 'chosen_samples': [45099, 28886, 55314, 39305, 16453, 17055, 20186, 31738, 718, 1075], 'chosen_samples_score': ['0.93389446', '0.95588136', '1.0184808', '1.0727013', '0.9698229', '1.0116771', '0.93631', '0.9949998', '0.9495916', '1.0012784']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9544, 'nll': 0.3570082917809486}, 'chosen_samples': [32323, 346, 8221, 23946, 46021, 31094, 25158, 54195, 33391, 1239], 'chosen_samples_score': ['0.946161', '0.947206', '0.95847845', '0.95044607', '0.9713011', '0.9489911', '0.98097575', '1.0083524', '1.023787', '1.067113']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.965, 'nll': 0.2941927626729012}, 'chosen_samples': [12018, 16860, 37044, 47220, 12268, 5042, 1518, 28491, 14664, 27358], 'chosen_samples_score': ['0.9156206', '0.9503402', '0.9165063', '0.952099', '0.9324996', '0.96310174', '1.0254772', '1.0202863', '0.9645076', '0.9840615']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9551, 'nll': 0.374967585504055}, 'chosen_samples': [47926, 13969, 17086, 30359, 51889, 1423, 29320, 19942, 36452, 1461], 'chosen_samples_score': ['0.8328451', '0.83807373', '0.8386581', '0.8426337', '0.8578984', '0.8639867', '0.91419506', '0.9965917', '0.86073625', '0.8644605']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9633, 'nll': 0.323149237036705}, 'chosen_samples': [5194, 27172, 23086, 46368, 20869, 7768, 28633, 59286, 33426, 5370], 'chosen_samples_score': ['0.94762343', '0.9520623', '0.9562202', '0.9606247', '0.9597027', '1.0038764', '1.0070975', '0.9802686', '0.9640646', '0.9686239']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.954, 'nll': 0.37041195631027224}, 'chosen_samples': [21438, 16472, 43474, 52225, 52169, 47132, 42986, 2292, 39429, 50317], 'chosen_samples_score': ['0.85573316', '0.85777944', '0.86441517', '0.866833', '0.9316009', '0.9364883', '0.93538535', '0.86823046', '0.9767347', '0.8861787']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9631, 'nll': 0.3326582103967667}, 'chosen_samples': [38246, 29711, 50308, 748, 34771, 11858, 10256, 14935, 55906, 29360], 'chosen_samples_score': ['1.0024372', '1.0026298', '1.0164118', '1.0986438', '1.0350587', '1.0875711', '1.0323257', '1.0684502', '1.0860977', '1.0374588']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9661, 'nll': 0.31975904405117034}, 'chosen_samples': [32747, 424, 50134, 45787, 16698, 5430, 25094, 3367, 56662, 250], 'chosen_samples_score': ['0.99064785', '0.9921986', '0.99273795', '1.0117874', '1.0289769', '1.0475587', '1.1110597', '1.033031', '1.0995865', '1.0842125']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9671, 'nll': 0.2824126422405243}, 'chosen_samples': [55244, 31345, 16043, 4955, 6289, 8196, 21990, 21164, 59321, 42828], 'chosen_samples_score': ['0.8428478', '0.85391694', '0.894204', '0.89569515', '0.93849695', '0.86697584', '0.8577588', '0.9187001', '0.8840201', '0.96646315']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9621, 'nll': 0.3180307373404503}, 'chosen_samples': [19814, 2490, 57972, 4475, 49463, 28628, 57718, 20110, 40208, 18324], 'chosen_samples_score': ['0.8748797', '0.90595615', '0.9093067', '0.8995842', '0.91615164', '0.93025076', '0.9417802', '0.9840788', '0.93106115', '0.9893716']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9656, 'nll': 0.3054446384310722}, 'chosen_samples': [6418, 22320, 59664, 9717, 44570, 48603, 26358, 49525, 17213, 49198], 'chosen_samples_score': ['0.91037273', '0.9538787', '0.92131025', '0.95534146', '0.9108576', '0.9429804', '0.9685137', '1.0581155', '1.0539987', '0.9717142']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.967, 'nll': 0.3082742586731911}, 'chosen_samples': [3524, 47247, 29991, 36744, 28844, 37048, 18598, 38608, 36439, 28368], 'chosen_samples_score': ['0.9300347', '0.9369219', '0.9300583', '0.9403891', '0.9410104', '0.95466346', '0.96086615', '1.1038721', '1.0396464', '1.0659645']})
store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9652, 'nll': 0.29894723668694495}, 'chosen_samples': [51993, 45424, 9677, 29120, 278, 45057, 50236, 24145, 53872, 44927], 'chosen_samples_score': ['1.0003495', '1.0068315', '1.0095056', '1.0264928', '1.0590458', '1.019002', '1.0185453', '1.0743794', '1.0576167', '1.0104063']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9653, 'nll': 0.30512065142393113}, 'chosen_samples': [21433, 39355, 52838, 8297, 36268, 30770, 41540, 23927, 43745, 24589], 'chosen_samples_score': ['0.88142717', '0.891635', '0.89611334', '0.94935733', '0.9001868', '0.93709856', '0.9487988', '0.941005', '0.94553655', '0.8955819']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9676, 'nll': 0.29420492202043536}, 'chosen_samples': [22530, 49589, 37147, 17540, 17296, 56014, 18003, 4850, 20150, 50514], 'chosen_samples_score': ['0.8567504', '0.8591122', '0.86373526', '0.8851121', '1.012212', '0.9350155', '0.86474437', '0.86767083', '0.9996857', '0.872297']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9668, 'nll': 0.28842645436525344}, 'chosen_samples': [39354, 50340, 11616, 15893, 44753, 20903, 13942, 15239, 32835, 37161], 'chosen_samples_score': ['0.91935295', '0.92075855', '0.9228459', '0.95472133', '1.0587486', '0.9705055', '1.0108831', '0.9881338', '0.93699926', '0.96195215']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9654, 'nll': 0.29140645265579224}, 'chosen_samples': [15913, 42337, 17478, 15781, 52087, 39320, 50090, 9552, 46832, 36421], 'chosen_samples_score': ['0.9091547', '0.9205709', '0.9357866', '0.94278663', '0.9440949', '0.95479906', '0.96578175', '0.97088206', '1.0260327', '0.9901727']})
store['iterations'].append({'num_epochs': 12, 'test_metrics': {'accuracy': 0.9649, 'nll': 0.3073055371642113}, 'chosen_samples': [43874, 3794, 1652, 53019, 20720, 15801, 15948, 13276, 995, 3798], 'chosen_samples_score': ['0.8955078', '0.8994515', '0.99038446', '0.9567274', '0.93867797', '0.9075916', '0.9502955', '0.94789356', '0.94041276', '0.9534046']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9704, 'nll': 0.2701971098780632}, 'chosen_samples': [54858, 8200, 24479, 32276, 21896, 13259, 24462, 966, 54880, 18501], 'chosen_samples_score': ['0.87020063', '0.8742699', '0.8769152', '0.8761014', '0.89173776', '0.8802613', '0.88108826', '0.90974265', '0.91239', '0.92338794']})
store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.9714, 'nll': 0.2647117182612419}, 'chosen_samples': [48102, 43702, 48681, 32880, 17079, 48038, 11600, 21390, 54950, 4822], 'chosen_samples_score': ['0.90956277', '0.9102698', '0.9252609', '0.9252818', '1.0888637', '0.9397411', '0.965724', '0.9304682', '0.9715052', '1.0787895']})
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